Optimization of Distribution Network Operation Strategy Based on EV Users’ Willingness for Power Regulation Engagement

Due to the inherent stochasticity, volatility, and intermittency of renewable energy sources, power systems with a high penetration of renewables frequently experience supply-demand imbalance issues. Electric vehicles (EVs), as flexible and controllable distributed resources, can participate in the optimization of distribution network operation strategies to provide ancillary services for the system, such as peak shaving and renewable energy accommodation. The increasing popularity of EVs offers significant flexibility potential to distribution networks. The assessment of their adjustable potential, considering user response willingness, forms the foundation for optimizing EV utilization within distribution networks. However, existing operation strategies often fall short of achieving the desired outcomes. Therefore, this paper proposes an optimization method for distribution network operation strategies that explicitly incorporates EV users’ willingness to participate in power regulation. An EV adjustable potential assessment model that accounts for user willingness is established. Finally, comparisons with scenarios disregarding user willingness demonstrate that the adjustable potential evaluated by the proposed method more realistically reflects actual conditions.

Authors

Institutions

Publication Details

Published
2025-07-25
DOI
https://doi.org/10.1109/aceee66257.2025.11148813
Citations
1
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of Distribution Network Operation Strategy Based on EV Users’ Willingness for Power Regulation Engagement

Y. Ji, Miao Yang, Hao Li, Fan Wang et al.
1 citations
Electric Vehicles and Infrastructure
0.73
article

Optimization of Distribution Network Operation Strategy Based on EV Users’ Willingness for Power Regulation Engagement

Y. Ji, Miao Yang, Hao Li, Fan Wang, Wenzhong Shi, Yunyun Xie
article en
1 citations

Abstract

Due to the inherent stochasticity, volatility, and intermittency of renewable energy sources, power systems with a high penetration of renewables frequently experience supply-demand imbalance issues. Electric vehicles (EVs), as flexible and controllable distributed resources, can participate in the optimization of distribution network operation strategies to provide ancillary services for the system, such as peak shaving and renewable energy accommodation. The increasing popularity of EVs offers significant flexibility potential to distribution networks. The assessment of their adjustable potential, considering user response willingness, forms the foundation for optimizing EV utilization within distribution networks. However, existing operation strategies often fall short of achieving the desired outcomes. Therefore, this paper proposes an optimization method for distribution network operation strategies that explicitly incorporates EV users’ willingness to participate in power regulation. An EV adjustable potential assessment model that accounts for user willingness is established. Finally, comparisons with scenarios disregarding user willingness demonstrate that the adjustable potential evaluated by the proposed method more realistically reflects actual conditions.

State Grid Corporation of China (China) (CN), Nanjing University of Science and Technology (CN)
Openalex Percentile: Top 23%
Electric Vehicles and Infrastructure
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.